FOMO: Fairness-Oriented Multi-objective Optimization

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. Our lab takes a different approach, developing flexible optimizers that characterize the error-fairness tradeoff surface by integrating multi-objective optimization into existing machine learning models.

How FOMO works. From La Cava GECCO 2023

Code

FOMO: Fairness-Oriented Multi-objective Optimization

Interfair: Intersectional Fairness using FOMO

Funding

NIH National Library of Medicine

Selected Papers

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models

William G. La Cava (2023)

Proceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO)

dl.acm.org  |  arXiv  |  pdf  |  code

Genetic programming approaches to learning fair classifiers

William La Cava, Jason H. Moore (2020)

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

dl.acm.org  |  arXiv  |  pdf  |  Best Paper Award